Anind K. Dey

Anind K. Dey
Carnegie Mellon University | CMU · Human-Computer Interaction Institute

PhD, MS CS, Georgia Tech

About

411
Publications
162,524
Reads
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38,229
Citations
Additional affiliations
January 2005 - present
Carnegie Mellon University
Position
  • Professor (Associate)

Publications

Publications (411)
Article
The COVID-19 pandemic upended college education and the experiences of students due to the rapid and uneven shift to online learning. This study examined the experiences of students with disabilities with online learning, with a consideration of surrounding stressors such as financial pressures. In a mixed method approach, we compared 28 undergradu...
Preprint
BACKGROUND Sedentary behavior (SB) is prevalent after abdominal cancer surgery, and interventions targeting perioperative SB could improve postoperative recovery and outcomes. We conducted a pilot study to evaluate the feasibility and preliminary effects of a real-time mobile intervention that detects and disrupts prolonged SB before and after canc...
Article
This paper presents a computational framework for modeling biobehavioral rhythms - the repeating cycles of physiological, psychological, social, and environmental events - from mobile and wearable data streams. The framework incorporates four main components: mobile data processing, rhythm discovery, rhythm modeling, and machine learning. We evalua...
Article
Feeling a sense of belonging is a central human motivation that has consequences for mental health and well-being, yet surprisingly little research has examined how belonging shapes mental health among young adults. In three data sets from two universities (exploratory study: N = 157; Confirmatory Study 1: N = 121; Confirmatory Study 2: n = 188 in...
Article
Background: The coronavirus disease 2019 (COVID-19) pandemic has broad negative impact on physical and mental health of people with chronic neurological disorders such as multiple sclerosis (MS). Objective: We present a machine learning approach leveraging passive sensor data from smartphones and fitness trackers of people with MS to predict the...
Article
Full-text available
Continuous passive sensing of daily behavior from mobile devices has the potential to identify behavioral patterns associated with different aspects of human characteristics. This paper presents novel analytic approaches to extract and understand these behavioral patterns and their impact on predicting adaptive and maladaptive personality traits. O...
Preprint
Full-text available
As new technology inches into every aspect of our lives, there is no place more likely to dramatically change in the future than the workplace. New passive sensing technology is emerging capable of assessing human behavior with the goal of promoting better cognitive and physical capabilities at work. In this article, we survey recent research on th...
Preprint
Full-text available
Perceived discrimination is common and consequential. Yet, little support is available to ease handling of these experiences. Addressing this gap, we report on a need-finding study to guide us in identifying relevant technologies and their requirements. Specifically, we examined unfolding experiences of perceived discrimination among college studen...
Article
Full-text available
Hospital readmissions impose an extreme burden on both health systems and patients. Timely management of the postoperative complications that result in readmissions is necessary to mitigate the effects of these events. However, accurately predicting readmissions is very challenging, and current approaches demonstrated a limited ability to forecast...
Article
We hypothesize that behavioral patterns of people are reflected in how they interact with their mobile devices and that continuous sensor data passively collected from their phones and wearables can infer their job performance. Specifically, we study day-today job performance (improvement, no change, decline) of N=298 information workers using mobi...
Article
Background Given possible impairment in psychomotor functioning related to acute cannabis intoxication, we explored whether smartphone-based sensors (e.g., accelerometer) can detect self-reported episodes of acute cannabis intoxication (subjective “high” state) in the natural environment. Methods Young adults (ages 18–25) in Pittsburgh, PA, who re...
Preprint
Continuous passive sensing of daily behavior from mobile devices has the potential to identify behavioral patterns associated with different aspects of human characteristics. This paper presents novel analytic approaches to extract and understand these behavioral patterns and their impact on predicting adaptive and maladaptive personality traits. O...
Article
Objective This study addresses mental health concerns among university students, examining cumulative stress exposure as well as resilience resources. Participants: Participants were 253 first- and second-year undergraduate students (age = 18.76; 49.80% male, 69% students of color) enrolled at a large western US university. Methods: Data were obtai...
Article
Passive mobile sensing for the purpose of human state modeling is a fast-growing area. It has been applied to solve a wide range of behavior-related problems, including physical and mental health monitoring, affective computing, activity recognition, routine modeling, etc. However, in spite of the emerging literature that has investigated a wide ra...
Article
Full-text available
This mixed-method study examined the experiences of college students during the COVID-19 pandemic through surveys, experience sampling data collected over two academic quarters (Spring 2019 n 1 = 253; Spring 2020 n 2 = 147), and semi-structured interviews with 27 undergraduate students. There were no marked changes in mean levels of depressive symp...
Article
Full-text available
Taking an action research approach, we engaged in fieldwork with school-based behavioral health care teams to: observe record keeping practices, design and deploy a prototype system addressing key challenges, and reflect on its use. We describe the challenges of capturing behavioral data using both paper and electronic records. Creating records of...
Article
Assessment of individuals' job performance, personalized health and psychometric measures are domains where data-driven ubiquitous computing will have a profound impact in the near future. Existing work in these domains focus on techniques that use data extracted from questionnaires, sensors (wearable, computer, etc.), or other traits to assess wel...
Chapter
Full-text available
This study analyzes patterns of physical, mental, lifestyle, and personality factors in college students in different periods over the course of a semester and models their relationships with students’ academic performance. The data analyzed was collected through smartphones and Fitbit. The use of machine learning models derived from the gathered d...
Article
The prevalence of mobile phones and wearable devices enables the passive capturing and modeling of human behavior at an unprecedented resolution and scale. Past research has demonstrated the capability of mobile sensing to model aspects of physical health, mental health, education, and work performance, etc. However, most of the algorithms and mode...
Preprint
Full-text available
BACKGROUND Cancer treatments can cause a variety of symptoms that impair quality of life and functioning but are frequently missed by clinicians. Smartphone and wearable sensors may capture behavioral and physiological changes indicative of symptom burden, enabling passive and remote real-time monitoring of fluctuating symptoms. OBJECTIVE The aim...
Article
Full-text available
Background Cancer treatments can cause a variety of symptoms that impair quality of life and functioning but are frequently missed by clinicians. Smartphone and wearable sensors may capture behavioral and physiological changes indicative of symptom burden, enabling passive and remote real-time monitoring of fluctuating symptoms Objective The aim o...
Preprint
Full-text available
This study analyzes patterns of physical, mental, lifestyle, and personality factors in college students in different periods over the course of a semester and models their relationships with students' academic performance. The data analyzed was collected through smartphones and Fitbit. The use of machine learning models derived from the gathered d...
Article
We present a machine learning approach that uses data from smartphones and fitness trackers of 138 college students to identify students that experienced depressive symptoms at the end of the semester and students whose depressive symptoms worsened over the semester. Our novel approach is a feature extraction technique that allows us to select mean...
Preprint
Full-text available
This paper presents CoRhythMo, the first computational framework for modeling biobehavioral rhythms - the repeating cycles of physiological, psychological, social, and environmental events - from mobile and wearable data streams. The framework incorporates four main components: mobile data processing, rhythm discovery, rhythm modeling, and machine...
Preprint
We present a study to detect friendship, its strength, and its change from smartphone location data collectedamong members of a fraternity. We extract a rich set of co-location features and build classifiers that detectfriendships and close friendship at 30% above a random baseline. We design cross-validation schema to testour model performance in...
Chapter
The latest smartphones have advanced sensors that allow us to recognize human and environmental contexts. They operate primarily on Android and iOS, and can be used as sensing platforms for research in various fields owing to their ubiquity in society. Mobile sensing frameworks help to manage these sensors easily. However, Android and iOS are const...
Preprint
The impact of COVID-19 on students has been enormous, with an increase in worries about fiscal and physical health, a rapid shift to online learning, and increased isolation. In addition to these changes, students with disabilities/health concerns may face accessibility problems with online learning or communication tools, and their stress may be c...
Article
Full-text available
Background Mobile assessment of the effects of acute marijuana on cognitive functioning in the natural environment would provide an ecologically valid measure of the impacts of marijuana use on daily functioning. Objective This study aimed to examine the association of reported acute subjective marijuana high (rated 0-10) with performance on 3 mob...
Preprint
BACKGROUND Sedentary behavior (SB) is common after cancer surgery and may negatively affect recovery and quality of life, but postoperative symptoms (e.g., pain) can be a significant barrier to patients achieving recommended physical activity levels. We conducted a single-arm pilot trial evaluating the usability and acceptability of a real-time mob...
Article
Full-text available
Background Sedentary behavior (SB) is common after cancer surgery and may negatively affect recovery and quality of life, but postoperative symptoms such as pain can be a significant barrier to patients achieving recommended physical activity levels. We conducted a single-arm pilot trial evaluating the usability and acceptability of a real-time mob...
Article
Full-text available
Several psychologists posit that performance is not only a function of personality but also of situational contexts, such as day-level activities. Yet in practice, since only personality assessments are used to infer job performance, they provide a limited perspective by ignoring activity. However, multi-modal sensing has the potential to character...
Article
User interfaces are important for streamlining the interactions between humans and computers. However, there are few effective approaches for collecting users’ preferences implicitly and objectively for the purpose of user interface (UI) design optimization. This paper presents an effective approach to interactive genetic algorithm (IGA) optimizati...
Article
Full-text available
A deep understanding of how discrimination impacts psychological health and well-being of students could allow us to better protect individuals at risk and support those who encounter discrimination. While the link between discrimination and diminished psychological and physical well-being is well established, existing research largely focuses on c...
Conference Paper
Parkinson's disease (PD) is the second most common neurodegenerative disorder, impacting an estimated seven to ten million people worldwide. Measuring the symptoms and progress of the disease, and medication effectiveness is currently performed using subjective measures and visual estimation. We developed and evaluated a mobile application, STOP fo...
Conference Paper
Improving mobile keyboard typing speed increases in value as more tasks move to a mobile setting. Autocorrect reduces the time it takes to manually fix typing errors, which results in typing speed increase. However, recent user studies uncovered an unexplored side-effect: participants' aversion to typing errors despite autocorrect. We present a com...
Article
The rate of depression in college students is rising, which is known to increase suicide risk, lower academic performance and double the likelihood of dropping out of school. Existing work on finding relationships between passively sensed behavior and depression, as well as detecting depression, mainly derives relevant unimodal features from a sing...
Article
Full-text available
The integration of 5G networks and AI benefits to create a more holistic and better connected ecosystem for industries. User profiling has become an important issue for industries to improve company profit. In the 5G era, smartphone applications have become an indispensable part in our everyday lives. Users determine what apps to install based on t...
Article
Full-text available
Smartphone applications (Abbr. apps) have become an indispensable part in our everyday lives. Users determine what apps to use depending on their personal needs and interests. Users with different attributes may have different needs, making it natural for their app usage behaviors to be different. The differences in app usage behaviors among users...
Conference Paper
Full-text available
Smartphone apps are becoming ubiquitous in our everyday life. Apps on smartphones sense users' behaviors and activities , providing a lens for understanding users, which is an important point in the community of ubiquitous computing. In UbiComp 2018, we successfully held the first International workshop AppLens 2018: mining and learning from smartp...
Article
The number and popularity of smartphone applications is rising dramatically. Users install and use applications depending on their needs and interests. Applications on smartphones convey lots of personal information, providing us a new lens to well profile users. In this paper, we first describe application information for user profiling. Second, w...
Conference Paper
Full-text available
The proliferation of sensors allows for continuous capturing an individual's physical, social, and environmental contexts. We apply machine learning to sensor-collected data to analyze and predict personality, a factor known to influence job performance. Based on our work in Tesserae project, an ongoing study of 757 workers in multi-companies, we p...
Conference Paper
People eat every day and biting is one of the most fundamental and natural actions that they perform on a daily basis. Existing work has explored tooth click location and jaw movement as input techniques, however clenching has the potential to add control to this input channel. We propose clench interaction that leverages clenching as an actively c...
Chapter
Full-text available
Coproduction is an important form of service exchange in local community where members perform and receive services among each other on non-profit basis. Local coproduction systems enhance community connections and re-energize neighborhoods but face difficulties matching relevant and convenient transaction opportunities. Context-aware recommendatio...
Article
Biobehavioral rhythms are associated with numerous health and life outcomes. We study the feasibility of detecting rhythms in data that is passively collected from Fitbit devices and using the obtained model parameters to predict readmission risk after pancreatic surgery. We analyze data from 49 patients who were tracked before surgery, in hospital...
Data
This is a poster when I presented this research at the conference (MobileHCI 2018)
Preprint
BACKGROUND Loneliness significantly affects the quality of life and physical and mental health. In addition, recent studies have shown high levels of loneliness across various populations ranging from older adults to college students. Detection of loneliness through passive sensing on personal devices can lead to a better understanding of measurabl...
Article
Background: Feelings of loneliness are associated with poor physical and mental health. Detection of loneliness through passive sensing on personal devices can lead to the development of interventions aimed at decreasing rates of loneliness. Objective: The aim of this study was to explore the potential of using passive sensing to infer levels of lo...
Conference Paper
Full-text available
Parkinson's disease (PD) is the second most common neurodegenerative disorder, impacting an estimated seven to ten million people worldwide. It is commonly accepted that improving medication adherence alleviates symptoms and maintains motor capabilities. Not following the medication regimen (e.g., skipping or over-medicating) may worsen side-effect...
Article
Full-text available
Hypertension is a common and chronic disease, caused by high blood pressure. Since hypertension often has no warning signs or symptoms, many cases remain undiagnosed. Untreated or sub-optimally controlled hypertension may lead to cardiovascular, cerebrovascular and renal morbidity and mortality, along with dysfunction of the autonomic nervous syste...
Conference Paper
Full-text available
Smartphone applications (abbr. apps) are becoming ubiquitous in our everyday life. Apps on smartphones can sense users' behaviors and activities, providing a lens for understanding users, which is an important point in the community of ubiquitous computing. In UbiComp 2018, we would like to run a workshop on mining and learning from smartphone apps...
Conference Paper
Full-text available
The spread of smartphones allows us to freely capture video and diverse hardware sensors' data (e.g., accel erometer, gyroscope). While recording such data is relatively simple, it is often challenging to share and restream this data to other people and applications. Such capability is very valuable for a range of applications such as a context-awa...
Poster
This is a poster of "Senbay: A Platform for Instantly Capturing, Integrating, and Restreaming of Synchronized Multiple Sensor-Data Stream"
Chapter
In this paper we have investigated a range of multi-modal displays (visual, auditory, haptic) to understand the effects of interruptions across various modalities on response times. Understanding these effects is particularly relevant in complex tasks that require perceptual attention, where pertinent information needs to be delivered to a user, e....
Preprint
Full-text available
Activity recognition is inherent to the vision of Internet-of-Things-enabled smart environments and enables end-users to perform activities of interest and have their smart environments respond appropriately. However as smart environments evolve and are expanded over time, the effort that end-users and others put into training their activity recogn...
Article
Full-text available
A user task is often distributed across devices, e.g., a student listening to a lecture in a classroom while watching slides on a projected screen and making notes on her laptop, and sometimes checking Twitter for comments on her smartphone. In scenarios like this, users move between heterogeneous devices and have to deal with task resumption overh...
Article
This study aims to explore usability issues of watch-type wearable devices and to suggest guidelines for improved operation of smartwatches. To do so, we conducted a series of surveys, interviews, and task performance experiments. Thirty smartwatch users from ages 20 to 43 years were recruited. Users’ experiences of smartwatches were collected via...
Article
Full-text available
Since 2010, annual StarCraft AI competitions have promoted the development of successful AI players for complex real-time strategy games. In these competitions, AI players are ranked based on their win ratio over thousands of head-to-head matches. Although simple and easily implemented, this evaluation scheme may less adequately help develop more h...
Article
A large plethora of models to predict human mobility exists in the literature. The problem of how to select the most appropriate model to solve a specific mobility prediction task has however received only little attention. Yet a wrong model choice may lead to severe performance losses. In this paper, we address the model selection problem in human...